In today's society an activity is normally performed among a group of people. Such activity may be, e.g., movie watching or dining. Given a list of service items and each user's preference for the service items in the list, a group recommendation can be performed.
Traditional approaches to provide group recommendations combine user preferences in the user group by using one or more score aggregation techniques such as average, least-misery, greatest pleasure, average pair-wise disagreement etc. These approaches all work by first calculating estimated ratings for each service user and service item and by then using a formula to aggregate ratings of the service users in the user group.
However, normally different people have different influences on the choice of the group recommendation. Here, the problem is to find out how much each individual in the group can influence the choice and how to capture the group dynamics of the service selection.
In view of the above, the problem underlying the present invention is to enhance the group recommendation by capturing previous user selections.